Diversity metrics are computed following the methods of previous work [1]}, [2]}, which measure the distributional spread of generated dances in the “kinetic” (Dist\(_k\) ) and “geometric” (Dist\(_g\) ) feature spaces, as implemented by fairmotion [3]}, [4]}, [5]}.
We compute these metrics on 5-second dance clips produced by each approach.
Given that the ultimate goal of dance generation is to automatically produce dances that emulate the ground truth distribution, we argue that models should aim to match the scores of the ground truth distribution rather than maximize their absolute values.
Indeed, past work has found that jittery dances result in high diversity scores, in some cases exceeding the ground truth [6]}, [1]}.
